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import gradio as gr
#import torch
#from torch import autocast // only for GPU
from PIL import Image
import numpy as np
from io import BytesIO
import os
MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD')
from diffusers import StableDiffusionImg2ImgPipeline
print("hello sylvain")
YOUR_TOKEN=MY_SECRET_TOKEN
device="cpu"
#prompt_pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", use_auth_token=YOUR_TOKEN)
#prompt_pipe.to(device)
img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", use_auth_token=YOUR_TOKEN)
img_pipe.to(device)
source_img = gr.Image(source="upload", type="filepath", label="init_img | 512*512 px")
gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[2], height="auto")
def resize(value,img):
#baseheight = value
img = Image.open(img)
#hpercent = (baseheight/float(img.size[1]))
#wsize = int((float(img.size[0])*float(hpercent)))
#img = img.resize((wsize,baseheight), Image.Resampling.LANCZOS)
img = img.resize((value,value), Image.Resampling.LANCZOS)
return img
def infer(prompt, source_img):
source_image = resize(512, source_img)
source_image.save('source.png')
images_list = img_pipe([prompt] * 2, init_image=source_image, strength=0.75)
images = []
safe_image = Image.open(r"unsafe.png")
for i, image in enumerate(images_list["sample"]):
if(images_list["nsfw_content_detected"][i]):
images.append(safe_image)
else:
images.append(image)
return images
print("Great sylvain ! Everything is working fine !")
title="Img2Img Stable Diffusion CPU"
description="Img2Img Stable Diffusion example using CPU and HF token. <br />Warning: Slow process... ~5/10 min inference time. <b>NSFW filter enabled.</b>"
gr.Interface(fn=infer, inputs=["text", source_img], outputs=gallery,title=title,description=description).queue(max_size=100).launch(enable_queue=True)
#from torch import autocast
#import requests
#import torch
#from PIL import Image
#from io import BytesIO
#import os
#MY_SECRET_TOKEN = os.environ.get('HF_TOKEN_SD')
#from diffusers import StableDiffusionImg2ImgPipeline
#YOUR_TOKEN = MY_SECRET_TOKEN
# load the pipeline
#device = "cuda"
#model_id_or_path = "CompVis/stable-diffusion-v1-4"
# pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token = YOUR_TOKEN)
#pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
# model_id_or_path,
# revision="fp16",
# torch_dtype=torch.float16,
# use_auth_token=YOUR_TOKEN
#)
# or download via git clone https://huggingface.co/CompVis/stable-diffusion-v1-4
# and pass `model_id_or_path="./stable-diffusion-v1-4"` without having to use `use_auth_token=True`.
#pipe = pipe.to(device)
# let's download an initial image
#url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
#response = requests.get(url)
#init_image = Image.open(BytesIO(response.content)).convert("RGB")
#init_image = init_image.resize((768, 512))
#prompt = "Lively, illustration of a [[[<king::4>]]], portrait, fantasy, intricate, Scenic, hyperdetailed, hyper realistic <king-hearthstone>, unreal engine, 4k, smooth, sharp focus, intricate, cinematic lighting, highly detailed, octane, digital painting, artstation, concept art, vibrant colors, Cinema4D, WLOP, 3d render, in the style of hearthstone::5 art by Artgerm and greg rutkowski and magali villeneuve, martina jackova, Giger"
#with autocast("cuda"):
# images = pipe(prompt=prompt, init_image=init_image, strength=0.75, guidance_scale=7.5).images
#images[0].save("fantasy_landscape.png") |